论文标题
生成的gaitnet
Generative GaitNet
论文作者
论文摘要
了解解剖学与总数之间的关系是成功预测步态模拟的关键。在这篇论文中，我们介绍了生成的Gaitnet，它是基于深度增强学习的新型网络架构，用于控制具有304个Hill型Mus-Mus-ulotendons的全面，全身，肌肉骨骼模型。生成步态是一种预先训练的人工神经网络的内部研究系统，在618维的解剖结构（例如，质量分布，身体比例，骨骼畸形和肌肉缺陷）和步态管道（例如，质量分布，身体比例，骨骼畸形和肌肉缺陷）和步态管道（例如，步伐，步伐和cad）。预先训练的步态网络采用了解剖和步态条件，并通过基于物理学的模拟为一系列适合于研究的步态循环。我们将阐明Genererative Gaitnet在基于实时物理学的模拟中产生各种健康和人体步态的功效和表达能力。
Understanding the relation between anatomy andgait is key to successful predictive gait simulation. Inthis paper, we present Generative GaitNet, which isa novel network architecture based on deep reinforce-ment learning for controlling a comprehensive, full-body, musculoskeletal model with 304 Hill-type mus-culotendons. The Generative Gait is a pre-trained, in-tegrated system of artificial neural networks learnedin a 618-dimensional continuous domain of anatomyconditions (e.g., mass distribution, body proportion,bone deformity, and muscle deficits) and gait condi-tions (e.g., stride and cadence). The pre-trained Gait-Net takes anatomy and gait conditions as input andgenerates a series of gait cycles appropriate to theconditions through physics-based simulation. We willdemonstrate the efficacy and expressive power of Gen-erative GaitNet to generate a variety of healthy andpathologic human gaits in real-time physics-based sim-ulation.
